Why use Process Configuration Mining for data quality?
Cleaning up bad data without addressing the underlying causes is a losing battle. In most Salesforce Orgs, data quality issues aren’t random—they are engineered into the system through uncontrolled record creation paths, overlapping automations, and overexposed access rights. Traditional clean-up campaigns often result in short-lived wins because the sources of corruption are still active.
Process Configuration Mining provides the X-ray vision needed to diagnose the real process and configuration flaws that allow poor data to enter and spread. It enables you to go beyond surface-level fixes and instead change how your Org actually works—creating long-term, sustainable data governance.
Think of it like this: Trying to clean up bad data without Process Configuration Mining is like trying to clean a lake while a factory upstream continues to dump waste into the water. Unless you identify and shut off the source, the pollution—and the clean-up work—never ends.
When to use Process Configuration Mining for data quality and governance?
Use Process Configuration Mining when struggling with persistent issues such as inconsistent data, duplicate records, or uncontrolled field values. It is essential when multiple teams or systems can create or modify records through different paths, but there is no central visibility into how or why.
It is also critical when maturing your Org’s data governance posture or preparing for the adoption of Agentforce or other AI-driven solutions, where high-quality, well-governed data is essential.
Prerequisites
Enterprise plan with Process Configuration Mining enabled
Perform Data Quality Root Cause Analysis with Process Configuration Mining
Step 1: Generate a Process Configuration Diagram
Begin by generating business process diagrams for the object where you suffer the most from poor data quality. If you are unsure if you even have poor data in your Org, pick your most business critical objects and generate diagrams for those.
Step 2: Investigate Number of Creation Paths
Focus on the first column of the diagram, which reveals every method for record creation. Identify quick actions, global actions, flows, related list creations, and any automated record introductions. Streamlining these paths helps enforce consistent data creation standards.
Best practices:
Less ways to create a new record means greater control, less variability, and easier maintenance
If you have multiple automations and actions to create a new record, make sure each serves unique purpose and that there are no overlaps.
Step 2: Identify Conflicting or Overlapping Automations and Logic
Analyze the business outcomes of the 'create' activities. Spot overlapping, conflicting, or inconsistent business logic, and plan to consolidate or retire outdated automations to improve consistency.
Step 3: Audit Who Can Modify Records
Examine all activities that move a record through its lifecycle, such as stage or status changes. Review the Human Resources assigned to each of these steps.
Ask yourself and the stakeholders: do we really want '...' to be able to modify these records?
Step 4: Analyze Validation Rule Coverage
Use the diagram to see where validation rules apply—or don’t. In many Orgs, critical validations only trigger late in the process (e.g., at "Closed Won" on Opportunities). Missing validations earlier in the process allow poor-quality data to persist. Strengthen validation rule coverage progressively throughout key process stages.
Step 5: Raise Stories to Take Action
Raise Stories or Business Requirements directly from diagram steps to document necessary changes—simplifying creation methods, tightening permissions, strengthening validations, and retiring unnecessary automations.
Step 6: Use Linked Metadata to Guide Fixes
Leverage the metadata links attached to each process step to identify the metadata that need to be changed or deprecated. Open them one by one and make necessary changes.